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Course Information

name :

0A007G

title :

Introduction to IBM SPSS Modeler and Data Mining (v18)

category :

Analytics

vendor :

IBM

classroomDeliveryMethod :

Classroom IBM

descriptions :

description :

OverviewThis course provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.AudienceAnyone who wants to become familiar with IBM SPSS Modeler.PrerequisitesGeneral computer literacy.ObjectivePlease refer to course overview.Course Outline1: Introduction to data mining• List two applications of data mining• Explain the stages of the CRISP-DM process model• Describe successful data-mining projects and the reasons why projects fail• Describe the skills needed for data mining

2: Working with IBM SPSS Modeler• Describe the MODELER user-interface• Work with nodes• Run a stream or a part of a stream• Open and save a stream• Use the online Help

3: Creating a data-mining project• Explain the basic framework of a data-mining project• Build a model• Deploy a model

5: Understanding the data• Audit the data• Explain how to check for invalid values• Take action for invalid values• Explain how to define blanks

6: Setting the unit of analysis• Set the unit of analysis by removing duplicate records• Set the unit of analysis by aggregating records• Set the unit of analysis by expanding a categorical field into a series of flag fields

8: Deriving and reclassifying fields• Use the Control Language for Expression Manipulation (CLEM)• Derive new fields• Reclassify field values

9: Identifying relationships• Examine the relationship between two categorical fields• Examine the relationship between a categorical field and a continuous field• Examine the relationship between two continuous fields

10: Introduction to modeling• List three modeling objectives• Use a classification model• Use a segmentation model

overview :

[This course provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.]

abstract :

This course provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.

prerequisits :

objective :

Overview
This course provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.
Audience
Anyone who wants to become familiar with IBM SPSS Modeler.
Prerequisites
General computer literacy.
Objective
Please refer to course overview.

5: Understanding the data• Audit the data• Explain how to check for invalid values• Take action for invalid values• Explain how to define blanks

6: Setting the unit of analysis• Set the unit of analysis by removing duplicate records• Set the unit of analysis by aggregating records• Set the unit of analysis by expanding a categorical field into a series of flag fields

8: Deriving and reclassifying fields• Use the Control Language for Expression Manipulation (CLEM)• Derive new fields• Reclassify field values

9: Identifying relationships• Examine the relationship between two categorical fields• Examine the relationship between a categorical field and a continuous field• Examine the relationship between two continuous fields

10: Introduction to modeling• List three modeling objectives• Use a classification model• Use a segmentation model